Supply-chain network design via simulation, optimization, and greenfield analysis.
Scored across 12 tools
The tools are cleanly separated into greenfield, optimization, simulation, and reference families, with list/describe/get or run steps that are easy to distinguish. The explain_* tools and get_sc_theory are all static text, but their stated domains are distinct enough that confusion is unlikely.
All tools use snake_case with a clear verb_noun pattern such as list_greenfield_demos, describe_opt_demo, get_opt_result, and run_simulation. The prefixes consistently signal the operation type across the entire set.
Twelve tools is well within a reasonable range and each family has a coherent set of operations: list, describe, get result, and explain. No tool appears redundant or arbitrarily added.
The server covers listing, describing, retrieving saved results, running saved simulation data, and providing explanatory/reference text for its three main areas. A minor gap is the lack of a dedicated model-description tool for simulation models, since list_models only gives an id and short description before run_simulation.
Detail on one greenfield demo: region, customer count, the saved DC counts and the score curve. Use before get_greenfield_result.
Inputs: demo_id (required).
Detail on one optimization demo: what it shows, its source, and every saved scenario key. Use before get_opt_result.
Inputs: demo_id (required).
Primer on greenfield analysis: center-of-gravity (Weber) siting, Weiszfeld's algorithm, location-allocation for N facilities, service constraints, and when to use greenfield versus facility selection. Static text.
Inputs: no named parameters.
Primer on supply chain network optimization: mixed-integer programming, decision variables and constraints, landed-cost objectives, the common problem classes, and when to optimize versus simulate. Static text.
Inputs: no named parameters.
Primer on supply chain simulation: when to simulate, optimize or both, how a discrete-event network model works, which numbers the engine reports, and the limits of a single run. Static text; call run_simulation for figures.
Inputs: no named parameters.
Return the saved greenfield result for one DC count, verbatim: the sited DCs (lat/lon, nearest city) and which customers each serves. Quote the numbers verbatim.
Inputs: demo_id (required), dc_count (required).
Return the saved optimization result for one scenario of a demo, verbatim: sourcing, production and transport decisions, costs, and facility open/close decisions. Call describe_opt_demo first for the valid scenario keys. Quote the numbers verbatim.
Inputs: demo_id (required), scenario_key.
Reference text from the SC Builder engine: event-driven model, ordering policies (pull, push, planned), the forecast-demand and order-point formulas, bill of materials, routing and supplier selection. Static text.
Inputs: no named parameters.
List the saved greenfield siting demos with an id and summary each. Use before describe_greenfield_demo or get_greenfield_result.
Inputs: no named parameters.
List the sample supply chain models run_simulation has a saved engine run for, with each model's id and a short description.
Inputs: no named parameters.
List the saved optimization demos (Tariff sourcing, Coffee co-pack siting, Safety Stock Optimization) with an id and summary each. Use before describe_opt_demo or get_opt_result.
Inputs: no named parameters.
Return the saved SC Builder engine run of a sample supply chain model: metrics, orders, shipments, routing and inventory series, verbatim. This server runs no engine; a model without a saved run answers 'not available here'. Quote the numbers verbatim.
Inputs: model_id (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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